nadiaazri,Nadia Azri,github

Hey 👋, I am a Ph.D. candidate specializing in Artificial Intelligence and Image Processing, concurrently serving as a web developer. With a Master's in Computer Science in the Image and Artificial life field, my expertise spans image processing, computer graphics, ML/DL, and ongoing research initiatives, including a focus on harnessing AI advancements for cancer diagnosis. Proficient in Django/Python, HTML, CSS, and JavaScript for web development, I bring strong collaboration and communication skills. Known for quick learning and solving complex technical challenges, I am committed to continuous skill improvement and contributing to innovative projects.

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Nadia Azri's Projects

amazon_price_monitoring

The Amazon Price Monitor is a Python program for tracking and alerting users about price changes on Amazon. It scrapes real-time data from specified product pages, storing details in a CSV file. With email notifications, users are promptly informed when product prices drop below set thresholds, ensuring timely and informed purchasing decisions.

cascaded_soft_shadow_maps

Physically accurate soft shadows using area light source and Shadow maps

chat-room-application

i've build a simple chat application ( i'm still working on it) using django channels and websockets...

cook_recipe_api

An API post cooking recipes and give feedback and comments to this recipes...

don-t-die-please

2D Game using Unity3d, c#

featureselection-geneticalgorithm

Dive into feature selection and classification with this Python repository, utilizing a genetic algorithm and various classifiers on a breast cancer dataset. Achieving high accuracy levels, SVM and ANN stand out with 97.37%. Ideal for machine learning enthusiasts and those interested in cancer diagnostics.

imdb_scraping

IMDb data of all the movies and TV shows released on 2020

research-papers-breast-cancer-clinical-dataset

This repository contains one of my Google Sheet files and a conference paper that has been accepted in ISPA IEEE, The Sheet used for organizing research papers related to breast cancer analysis. The focus of the papers is on the utilization of clinical datasets and machine/deep learning techniques. The collection spans the period from 2020 to 2023.

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